Anomaly detection using image-based physical characterization

ABSTRACT

An aspect of the invention includes reading a scale in image data representing an image of physical characteristics and resizing at least a portion of the image data to align with target image data representing a target image based at least in part on the scale to form resized image data representing one or more resized images. Noise reduction is applied to the resized image data to produce test image data representing one or more test images. A best fit analysis is performed on the test image data with respect to the target image data. Test image data having the best fit are stored with training image data representing classification training images indicative of one or more recognized features. An anomaly in unclassified image data representing an unclassified image is identified based at least in part on an anomaly detector as trained using the classification training images.

BACKGROUND

The present invention relates to component inspection, and morespecifically, to anomaly detection using image-based physicalcharacterization.

Visual inspection methods can capture images for components to beinspected and determine pass/fail criteria or identify whether one ormore various anomaly types are present. In complex systems, there can bemany components to inspect for a wide range of anomalies. Images can becaptured under different conditions depending upon an operator, imagecapturing devices used, and other such factors.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for anomaly detection. A non-limitingexample of the method includes reading, by a processor, a scale in imagedata representing an image of a plurality of physical characteristics.The processor resizes at least a portion of the image data to align withtarget image data representing a target image of one or more structuresbased at least in part on the scale to form resized image datarepresenting one or more resized images. The processor applies noisereduction to the resized image data to produce test image datarepresenting one or more test images and performs a best fit analysis onthe test image data with respect to the target image data. Test imagedata of at least one of the test images having the best fit are storedwith training image data representing a plurality of classificationtraining images indicative of one or more recognized features. Ananomaly in unclassified image data representing an unclassified image isidentified based at least in part on an anomaly detector as trainedusing the classification training images.

Embodiments of the present invention are directed to a computer programproduct for anomaly detection. The computer program product includes acomputer readable storage medium readable by a processing circuit andstoring program instructions for execution by the processing circuit forperforming a method. A non-limiting example of the method includesreading a scale in image data representing an image of a plurality ofphysical characteristics and resizing at least a portion of the imagedata to align with target image data representing a target image of oneor more structures based at least in part on the scale to form resizedimage data representing one or more resized images. Noise reduction isapplied to the resized image data to produce test image datarepresenting one or more test images. A best fit analysis is performedon the test image data with respect to the target image data. Test imagedata of at least one of the test images having the best fit are storedwith training image data representing a plurality of classificationtraining images indicative of one or more recognized features. Ananomaly in unclassified image data representing an unclassified image isidentified based at least in part on an anomaly detector as trainedusing the classification training images.

Embodiments of the present invention are directed to a processing systemfor anomaly detection. The processing system includes one or more typesof memory and at least one processor communicatively coupled with theone or more types of memory. The at least one processor is configured toperform a method. A non-limiting example of the method includes readinga scale in image data representing an image of a plurality of physicalcharacteristics and resizing at least a portion of the image data toalign with target image data representing a target image of one or morestructures based at least in part on the scale to form resized imagedata representing one or more resized images. Noise reduction is appliedto the resized image data to produce test image data representing one ormore test images. A best fit analysis is performed on the test imagedata with respect to the target image data. Test image data of at leastone of the test images having the best fit are stored with trainingimage data representing a plurality of classification training imagesindicative of one or more recognized features. An anomaly inunclassified image data representing an unclassified image is identifiedbased at least in part on an anomaly detector as trained using theclassification training images.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a block diagram illustrating one example of a processingsystem for practice of the teachings herein;

FIG. 2 depicts a system according to embodiments of the presentinvention;

FIG. 3 depicts a system according to embodiments of the presentinvention;

FIG. 4 depicts a data flow according to one or more embodiments of thepresent invention;

FIG. 5A depicts a portion of a process for anomaly detection accordingto one or more embodiments of the present invention;

FIG. 5B depicts a portion of a process for anomaly detection accordingto one or more embodiments of the present invention;

FIG. 6 depicts an example image for analysis according to one or moreembodiments of the present invention;

FIG. 7 depicts an image cut example of an image according to one or moreembodiments of the present invention;

FIG. 8 depicts a target image for analysis according to one or moreembodiments of the present invention;

FIG. 9 depicts a test image according to one or more embodiments of thepresent invention;

FIG. 10 depicts a test image according to one or more embodiments of thepresent invention;

FIG. 11 depicts a test image according to one or more embodiments of thepresent invention;

FIG. 12 depicts a test image according to one or more embodiments of thepresent invention;

FIG. 13 depicts a target image rotated according to one or moreembodiments of the present invention;

FIG. 14 depicts a schematic illustration of a post-processed imageidentified as good according to one or more embodiments of the presentinvention;

FIG. 15 depicts a schematic illustration of a post-processed imageidentified as an open circuit according to one or more embodiments ofthe present invention;

FIG. 16 depicts a schematic illustration of a post-processed imageidentified as a short circuit according to one or more embodiments ofthe present invention;

FIG. 17 depicts a schematic illustration of a post-processed imageidentified as a tapered structure according to one or more embodimentsof the present invention; and

FIG. 18 depicts a schematic illustration of a post-processed imageidentified as having thin work function metal according to one or moreembodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedescribed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” can include any integer number greater than or equalto one, i.e. one, two, three, four, etc. The terms “a plurality” caninclude any integer number greater than or equal to two, i.e. two,three, four, five, etc. The term “connection” can include both anindirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, a number of challenges can arisein performing recognition of physical characteristics in digital images.For example, when using a target image to compare with other images toidentify similar features, there can be differences due to imagerotation, image scale, image resolution, image noise, image intensity,and other such differences. Further, known visual recognition techniquesthat work well with color images cannot work well with gray-scale images(also referred to as black-and-white images). Some image capturingtechniques use alternatives to visible light, such as X-ray basedimaging or transmission electron microscopy (TEM). These alternativeimage capturing techniques can include a larger degree of noise orblurriness as compared to cameras that operate in the visible lightspectrum.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing processing of images with varying resolutionand orientation for developing training sets of various identifiedphysical characteristics to support anomaly detection using machinelearning. A number of sample images can be captured from an imagingdevice with sub-images extracted and normalized to construct a trainingimage repository of classification training images that best fit atarget image of one or more structures. The best fit can be determinedby performing a best fit analysis. In embodiments of the invention, thebest fit analysis can include image shifting and/or rotation. Theprocess can be repeated for multiple target images having variousphysical characteristics indicative of “good” and anomalous features tocollect groups of corresponding classification training images. Onceimage processing and collection of classification training images areperformed, an anomaly detector can be trained using machine learningbased at least in part on the training image repository. After training,the anomaly detector can be used to identify one or more anomalies in anunclassified image, for instance, during a visual inspection process ofa component or article under analysis.

The above-described aspects of the invention address the shortcomings ofthe prior art by combining a number of image processing approaches tobuild a normalized training set for machine learning to support ananomaly detector. For example, identification of scaling information onan image itself can support resizing/rescaling of images. Randomsampling of images by taking cuts proportional to a target image, alongwith noise reduction can be used to generate multiple test images froman original image. A test image most closely aligning to a target imagecan be identified by rotation and performing a best fit comparison,which can include image shifting and/or rotation. Multiple iterationscan be performed to develop the training image repository ofclassification training images for test images indicative of recognizedfeatures with multiple classified physical characteristics. Technicalbenefits include enhanced anomaly detection for various physicalcharacteristics captured in image data. The described processes canefficiently analyze gray-scale images, such as TEM images, where colorimage processing techniques are not as effective and would typicallyrequire a larger number of training sets as compared to embodimentsdescribed herein.

Turning now to a more detailed description of aspects of the presentinvention, FIG. 1 depicts a processing system 100 according toembodiments of the invention. In the example of FIG. 1, the system 100has one or more central processing units (processors) 101 a, 101 b, 101c, etc. (collectively or generically referred to as processor(s) 101).Each processor 101 can include a reduced instruction set computer (RISC)microprocessor. Processors 101 are coupled to system memory 114 andvarious other components via a system bus 113. Read only memory (ROM)102 is coupled to the system bus 113 and can include a basicinput/output system (BIOS), which controls certain basic functions ofsystem 100.

FIG. 1 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 can be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Software 120for execution on the processing system 100 can be stored in mass storage104. A network adapter 106 interconnects bus 113 with an outside network116 enabling data processing system 100 to communicate with other suchsystems. A screen (e.g., a display monitor) 115 is connected to systembus 113 by display adaptor 112, which can include a graphics adapter toimprove the performance of graphics intensive applications and a videocontroller. In one embodiment, adapters 107, 106, and 112 can beconnected to one or more I/O busses that are connected to system bus 113via an intermediate bus bridge (not shown). Suitable I/O buses forconnecting peripheral devices such as hard disk controllers, networkadapters, and graphics adapters typically include common protocols, suchas the Peripheral Component Interconnect (PCI). Additional input/outputdevices are shown as connected to system bus 113 via user interfaceadapter 108 and display adapter 112. A keyboard 109, mouse 110, andspeaker 111 all interconnected to bus 113 via user interface adapter108, which can include, for example, a Super I/O chip integratingmultiple device adapters into a single integrated circuit.

Thus, as configured in FIG. 1, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system such as the AIX® operatingsystem from IBM Corporation to coordinate the functions of the variouscomponents shown in FIG. 1. The processing system 100 can executeinstructions to perform a number of processes as further describedherein and can be incorporated in one or more image processing systems.

FIG. 2 depicts an image processing system 200 that includes theprocessing system 100 of FIG. 1. The image processing system 200includes an imaging system 202 that is communicatively coupled to theprocessing system 100 using, for example, wired, wireless, or opticalcoupling. In the example of FIG. 2, the imaging system 202 can includeany type of imaging device operable to record scaling information withincaptured images. The imaging system 202 can capture images of an articleunder analysis 204 including one or more features 206 (e.g., F1, F2,F3). The features 206 include physical characteristics that are at leastpartially observable in images captured by the imaging system 202. Thefeatures 206 can be separate components, interconnected components, orportions of the same component as part of the article under analysis204. As one example, the features 206 are portions of one or moreelectronic devices. The imaging system 202 can capture images asgray-scale images of varying scale and/or resolution. The orientation ofthe features 206 can vary depending on placement of the article underanalysis 204 relative to a field of view 208 of the imaging system 202and/or the underlying structure of the features 206.

FIG. 3 depicts an image processing system 300 according to an alternateembodiment. The image processing system 300 includes the processingsystem 100 of FIG. 1 and an imaging system 302. In contrast to thedirect viewing configuration of FIG. 2, the imaging system 302 is apass-through imager, where an emission source 304 emits an imaging beam306 through one or more lens systems 308 and the features 206 of thearticle under analysis 204 with a resulting field of view 310 capturedby an image recording system 312. The image recording system 312 iscommunicatively coupled to the processing system 100 using, for example,wired, wireless, or optical coupling. When embodied as a TEM system, theemission source 304 can be an electron gun. The lens systems 308 caninclude a combination of one or more objective lens, diffraction lens,intermediate lens, and projector lens, including optical and/or magneticlenses. The image recording system 312 can use film-based,charge-coupled device based image sensing, and/or other image sensingtechnologies known in the art. The imaging system 302 can capture imagesas gray-scale images of varying scale and/or resolution.

FIG. 4 depicts a data flow 400 of processing that can be performed bythe processing system 100 of FIG. 1 as part of the image processingsystem 200, 300 of FIGS. 2 and 3. Alternatively, a portion of the dataflow 400 can be distributed between multiple instances of the processingsystem 100 and can be performed at different periods of time. The dataflow 400 includes a training data generator 402 that develops a trainingimage repository 404 of classification training images 406 used to trainan anomaly detector 408 using machine learning. For example, a TEM imagecan show a fin structure surrounded by shallow trench isolation (STI).The classification training images 406 can include normalized imagesthat depict examples of fin structures with STI having various physicalcharacteristics. Training of the anomaly detector 408 using one or moreknown machine learning techniques can enable the anomaly detector 408 toclassify a similar structure as a fin and a surrounded structure as STIwhen examining an unclassified image 410. If there is any void at theSTI, the anomaly detector 408 can recognize the void as abnormal at theSTI and report an anomaly 412 identified after training. After theanomaly detector 408 is trained, the anomaly detector 408 can continueto analyze one or more unclassified image 410 of the article underanalysis 204 including one or more features 206 to determine whether ananomaly 412 is identified. The one or more unclassified image 410 can becaptured by the imaging system 202, 302 of FIGS. 2 and 3. The instanceof the article under analysis 204 analyzed by the anomaly detector 408can be different than one or more images of the article under analysis204 used as images 416 and target images 418 by the training datagenerator 402.

The training data generator 402 can perform image processing 414 usingone or more images 416 of the article under analysis 204 that includevarious physical characteristics. The image processing 414 can performnormalization operations to better align features of the images 416 withone or more target images 418. Upon performing normalization and bestfit matching between the images 416 and target images 418, the imageprocessing 414 outputs classification training images 406 for storage inthe training image repository 404. Further details of the imageprocessing 414 and identification of one or more anomaly 412 aredescribed in reference to FIGS. 5A and 5B.

Referring now to FIGS. 5A and 5B, a flow chart illustrating an exemplaryprocess 500 for anomaly detection according to one or more embodimentsof the present invention is shown. FIGS. 5A and 5B are described withrespect to FIGS. 1-4 and can be performed by the processing system 100as part of the image processing system 200, 300 of FIGS. 2 and 3.Further one or more portions of the process 500 can be distributedbetween multiple physical and/or virtual machines. In embodiments,processor 101 of the processing system 100 can refer to physical orvirtual processing resources on the same or different physical orvirtual machines. The process 500 is further described with respect toexample images depicted in FIGS. 6-18.

At block 502, processor 101 can read a scale in image data representingan image 600 of FIG. 6 of a plurality of physical characteristics as oneor more of the images 416 captured by the imaging system 202, 302 aspart of the image processing 414. The image 600 can include a scale bar602 that graphically depicts relative physical sizes of various portionsof the image 600. In the example of FIG. 6, the image 600 includes asubstrate 604 and a gate fin 606 as a gray-scale image with noise as canbe acquired by image processing system 300, where the article underanalysis 204 includes a plurality of electronic structures as features206. In some embodiments, the image processing 414 can determine thescale of the image 600 by reading pixel data of the image datarepresenting image 600 into a two-dimensional matrix, dissecting thetwo-dimensional matrix to retain a portion of the image data expected tographically depict scaling information (e.g., lower left corner), andanalyzing the portion of the image data expected to graphically depictscaling information. For instance, if the scale bar 602 includes aphysical length and a numerical size value, the image processing 414 canuse the information depicted in the scale bar 602 (from the portion ofthe image 600 expected to graphically depict scaling information) tobuild a training set of data to recognize a plurality of differentlegend labels. Thus, a training set can be collected for examples suchas a 10 nm scale, a 20 nm scale, a 100 nm scale, and so forth.

In embodiments, processor 101 can extract a plurality of image cuts fromthe image data representing image 600 as part of the image processing414. As an example, image 700 of FIG. 7 can be one of the images 416with a scale bar 702 similar to the scale bar 602 of FIG. 6. Image cuts704, 706, 708, 710 can be extracted from the image data representingimage 700 by identifying a central portion 712 of the image 700 andrandomly selecting a plurality of image blocks in proximity to thecentral portion 712 of the image 700. Although four image cuts 704-710are depicted in the example of FIG. 7, it will be understood that anynumber of two or more image cuts 704-710 can be extracted.

At block 504, processor 101 can resize at least a portion of the imagedata representing image 700, such as the image cuts 704-710, to alignwith image data representing a target image 800 of FIG. 8 (e.g., one ofthe target images 418) of one or more structures based at least in parton the scale to form resized image data representing one or more resizedimages as part of the image processing 414. The target image 800 canalso include a scale bar 802, similar to the scale bar 702 but have adifferent scale value. The image processing 414 can perform resizing,for instance, by selecting a size of the image blocks to match a pixelcount of the target image 800. As one example, a ratio of the scale bar702 to the scale bar 802 can be determined and used for selecting areasizes for each of the image cuts 704-710. For instance, the scaling ofimage 700 can be four times larger, eight times larger, ten timeslarger, etc. than the target image 800. Resolution adjustments can bemade to better match pixel count with physical sizing between the imagecuts 704-710 and the target image 800.

At block 506, processor 101 can apply a noise reduction to the resizedimage data representing one or more resized images (e.g., rescaled imagecuts 704-710) to produce test image data representing a plurality oftest images 904, 906, 908, 910 of FIGS. 9, 10, 11, and 12 as part of theimage processing 414. In the example of FIGS. 9-12, a minor noisereduction is performed. In some embodiments, noise reduction includesperforming edge detection on the resized image data representing one ormore resized images (which can be gray-scale image data representing oneor more gray-scale images) to identify a plurality of features, and twoor more different colors can be applied to the features to convert theresized image data into smoothed color image data to recognize thefeatures. For instance, a Canny filter can be used to highlight edgesand define areas for recoloring from gray-scale to a reduced palette ofcolors. Similar noise removal and re-coloring can be applied to a copyof the target image 800 to maintain sizing and format similarity withthe test images 904-910.

At block 508, processor 101 can perform a best fit analysis on the testimage data representing test images 904-910 with respect to the targetimage data representing target image 800 as part of the image processing414. The best fit analysis can include image shifting and/or rotation.Target image 1300 of FIG. 13 depicts an example of a rotated version ofthe target image 800 without scale bar 802 for comparison. The imageprocessing 414 can determine a difference value between test image dataof each of the test images 904-910 and target image data of the targetimage 800, incrementally rotate either the test image data representingtest images 904-910 or target image data representing the target image800, determine a difference value after rotation, and identify the bestfit as one of the test images 904-910 having a lowest difference valuein comparison to the target image data of the target image 800 afterrotation. Thus, no matter the orientation differences between the testimages 904-910 and the target image 800, the image processing 414 candiscover a best matching orientation and closest image match between thetest images 904-910 and the target image 800.

At block 510, test image data representing one or more of the testimages 904-910 having the best fit can be stored with training imagedata representing classification training images 406 indicative of oneor more recognized features. At block 512, the training image repository404 including a plurality of classification training images 406 can beformed based at least in part on identifying a plurality of test images904-910 as best fit with respect to a plurality of target images 418indicative of a plurality of recognized features. For example, thetraining image repository 404 can be formed by repeating the processsteps of blocks 502-512 for a plurality of test images 904-910 as bestfit with respect to a plurality of target images 418 indicative of aplurality of recognized features. The recognized features includephysical characteristics identified as ‘good’/expected results andexamples of known anomalies.

At block 514, the anomaly detector 408 can be trained using machinelearning based at least in part on the training image data representingthe classification training images 406 in the training image repository404. For example, one or more known machine learning techniques can beapplied to map groups of similar features in the training imagerepository 404 to support feature identification and classification asunclassified images 410 are received. Training for structural and defectrecognition can enable the anomaly detector 408 to both identifyfeatures within an image and determine which target type the featuremost closely matches (e.g., an open circuit, a short circuit, goodyield, and the like).

At block 516, an anomaly 412 can be identified in unclassified imagedata representing an unclassified image 410 based at least in part onthe anomaly detector 408 as trained using the classification trainingimages 406. A number of examples are depicted schematically in FIGS.14-18 after processing for noise removal. For instance, post-processedimage 1400 of FIG. 14 is an example of a ‘good’ or expected combinationof physical characteristics for features related to gate formation.Post-processed image 1500 of FIG. 15 is an example of an open circuit1502 as an anomaly in physical characteristics. Post-processed image1600 of FIG. 16 is an example of a short circuit 1602 as an anomaly inphysical characteristics. Post-processed image 1700 of FIG. 17 is anexample of a tapered structure 1702 as an anomaly in physicalcharacteristics. Post-processed image 1800 of FIG. 18 is an example ofthin work function metal 1802 as an anomaly in physical characteristics.Other example for various types of observable structures and associatedanomalies will be apparent to one of ordinary skill in the art.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user' s computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments described. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method for anomalydetection, the method comprising: reading, by a processor, a scale inimage data representing an image of a plurality of physicalcharacteristics; resizing, by the processor, at least a portion of theimage data to align with target image data representing a target imageof one or more structures based at least in part on the scale to formresized image data representing one or more resized images; applying, bythe processor, noise reduction to the resized image data to produce testimage data representing one or more test images; performing, by theprocessor, a best fit analysis on the test image data with respect tothe target image data; storing the test image data of at least one ofthe test images having the best fit with training image datarepresenting a plurality of classification training images indicative ofone or more recognized features; and identifying an anomaly inunclassified image data representing an unclassified image based atleast in part on an anomaly detector as trained using the classificationtraining images.
 2. The computer-implemented method of claim 1, whereinthe anomaly detector is trained for structural and defect recognitionusing machine learning based at least in part on the training image datarepresenting the classification training images.
 3. Thecomputer-implemented method of claim 1, wherein the scale is determinedby reading pixel data of the image data into a two-dimensional matrix,dissecting the two-dimensional matrix to retain a portion of the imagedata expected to graphically depict scaling information, and analyzingthe portion of the image data expected to graphically depict scalinginformation.
 4. The computer-implemented method of claim 3, whereinanalyzing the portion of the image data expected to graphically depictscaling information comprises building a training set of data torecognize a plurality of different legend labels.
 5. Thecomputer-implemented method of claim 1, further comprising: extracting,by the processor, a plurality of image cuts from the image data byidentifying a central portion of the image and randomly selecting aplurality of image blocks in proximity to the central portion of theimage.
 6. The computer-implemented method of claim 5, wherein resizingat least a portion of the image data comprises selecting a size of theimage blocks to match a pixel count of the target image data.
 7. Thecomputer-implemented method of claim 1, wherein the resized image datacomprise gray-scale image data representing one or more gray-scaleimages and applying noise reduction comprises: performing edge detectionon the gray-scale image data to identify a plurality of features; andapplying two or more different colors to the features to convert theresized image data into smoothed color image data to recognize thefeatures.
 8. The computer-implemented method of claim 1, whereinperforming the best fit analysis on the test image data with respect tothe target image data comprises: determining a difference value betweenthe test image data and the target image data; incrementally rotatingeither the test image data or the target image data and determining thedifference value after rotation; and identifying the best fit as thetest image data representing one of the test images having a lowestdifference value in comparison to the target image data after rotation.9. A computer program product for anomaly detection, the computerprogram product comprising: a computer readable storage medium readableby a processing circuit and storing program instructions for executionby the processing circuit for performing: reading a scale in image datarepresenting an image of a plurality of physical characteristics;resizing at least a portion of the image data to align with target imagedata representing a target image of one or more structures based atleast in part on the scale to form resized image data representing oneor more resized images; applying noise reduction to the resized imagedata to produce test image data representing one or more test images;performing a best fit analysis on the test image data with respect tothe target image data; storing the test image data of at least one ofthe test images having the best fit with training image datarepresenting a plurality of classification training images indicative ofone or more recognized features; and identifying an anomaly inunclassified image data representing an unclassified image based atleast in part on an anomaly detector as trained using the classificationtraining images.
 10. The computer program product of claim 9, whereinthe anomaly detector is trained for structural and defect recognitionusing machine learning based at least in part on the training image datarepresenting the classification training images.
 11. The computerprogram product of claim 9, wherein the scale is determined by readingpixel data of the image data into a two-dimensional matrix, dissectingthe two-dimensional matrix to retain a portion of the image dataexpected to graphically depict scaling information, and analyzing theportion of the image data expected to graphically depict scalinginformation.
 12. The computer program product of claim 11, whereinanalyzing the portion of the image data expected to graphically depictscaling information comprises building a training set of data torecognize a plurality of different legend labels.
 13. The computerprogram product of claim 9, wherein the program instructions are furtherexecutable to cause the processing circuit to: extract a plurality ofimage cuts from the image data by identifying a central portion of theimage and randomly selecting a plurality of image blocks in proximity tothe central portion of the image.
 14. The computer program product ofclaim 9, wherein the resized image data comprise gray-scale image datarepresenting one or more gray-scale images and applying noise reductioncomprises: performing edge detection on the gray-scale image data toidentify a plurality of features; and applying two or more differentcolors to the features to convert the resized image data into smoothedcolor image data to recognize the features.
 15. The computer programproduct of claim 9, wherein performing the best fit analysis on the testimage data with respect to the target image data comprises: determininga difference value between the test image data and the target imagedata; incrementally rotating either the test image data or the targetimage data and determining the difference value after rotation; andidentifying the best fit as the test image data representing one of thetest images having a lowest difference value in comparison to the targetimage data after rotation.
 16. A processing system for anomalydetection, comprising: one or more types of memory; and at least oneprocessor communicatively coupled with the one or more types of memory,the at least one processor configured to: read a scale in image datarepresenting an image of a plurality of physical characteristics; resizeat least a portion of the image data to align with target image datarepresenting a target image of one or more structures based at least inpart on the scale to form resized image data representing one or moreresized images; apply noise reduction to the resized image data toproduce test image data representing one or more test images; perform abest fit analysis on the test image data with respect to the targetimage data; store the test image data of at least one of the test imageshaving the best fit with training image data representing a plurality ofclassification training images indicative of one or more recognizedfeatures; and identify an anomaly in unclassified image datarepresenting an unclassified image based at least in part on an anomalydetector as trained using the classification training images.
 17. Theprocessing system of claim 16, wherein the anomaly detector is trainedfor structural and defect recognition using machine learning based atleast in part on the training image data representing the classificationtraining images.
 18. The processing system of claim 16, wherein the atleast one processor is configured to extract a plurality of image cutsfrom the image data by identifying a central portion of the image andrandomly selecting a plurality of image blocks in proximity to thecentral portion of the image.
 19. The processing system of claim 16,wherein the resized image data comprise gray-scale image datarepresenting one or more gray-scale images and the noise reductioncomprises: performing edge detection on the gray-scale image data toidentify a plurality of features; and applying two or more differentcolors to the features to convert the resized image data into smoothedcolor image data to recognize the features.
 20. The processing system ofclaim 16, wherein the best fit analysis performed on the test image datawith respect to the target image data is determined by: determining adifference value between the test image data and the target image data;incrementally rotating either the test image data or the target imagedata and determining the difference value after rotation; andidentifying the best fit as the test image data representing one of thetest images having a lowest difference value in comparison to the targetimage data after rotation.